D1.1 Service Discovery system: Load balancing mechanisms

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "D1.1 Service Discovery system: Load balancing mechanisms"

Transcription

1 D1.1 Service Discovery system: Load balancing mechanisms VERSION 1.0 DATE 2011 EDITORIAL MANAGER Eddy Caron AUTHORS STAFF Eddy Caron, Cédric Tedeschi Copyright ANR SPADES. 08-ANR-SEGI-025.

2

3 Contents Introduction 4 Load Balancing Algorithm 5 Simulations 7 Conclusion 11 ANR SPADES. 08-ANR-SEGI-025 Page 3

4 Introduction As we detail in Report 3.1, one key component of the architecture targeted by the SPADES project is Service Discovery. The architecture proposed, called DLPT (Distributed Lexicographic Placement Table), is shaped as a tree in which each node stores part of the service information. These nodes are labelled by the name of the service from which it stores the information. These labeled nodes are organized following the prefix patterns of labels. We detail in the same report how the nodes of the tree are dispatched among the nodes of the underlying overlay network whose processors (referred to as peers in the following) are logically connected into a ring. If the scale of the platform is tackled in Report 3.1 through the use of fully-distributed algorithms, another property of the targeted platforms is its dynamic nature and heterogeneity: The routing scheme presented in Report 3.1, as well as the heterogeneity on both popularity of keys and capacity of peers could lead to an unbalanced distribution of the load and thus create bottlenecks on different peers. The goal of the present report is to present algorithms developed tackling this issue. Its contribution is the design of a new load balancing heuristic based on the local maximization of the throughput. The throughput is defined as the number of requests processed by the service discovery system. This heuristic is inspired by works conducted on the load balancing within distributed hash tables. Through simulations, we have compared our heuristic to another heuristic for DHTs based on the K-choices paradigm, which is, to the best of our knowledge, the only purely-decentralized one that also assumes heterogeneity on both capacity of peers and popularity of keys, in a dynamic fashion. ANR SPADES. 08-ANR-SEGI-025 Page 4

5 Load Balancing Algorithm Each peer runs a set of nodes of the tree. As detailed in SPADES Report 1.3, the routing follows a top-down traversal. Therefore, the upper a node is, the more times it will be visited by some request. Also, due to possible sudden popularity of some service, the nodes storing the corresponding keys, independently from their depth in the tree, may become (strongly) overloaded. The heuristic presented in the following tends to maximize the aggregated throughput of two consecutive peers, i.e., the aggregated number of requests these two peers will be able to process. This is achieved by periodically redistributing the nodes on the peers, based on recent history. We consider a discrete time. For the sake of clarity, let us choose one particular peer S to illustrate the process. The load balancing process is triggered on S at each time step. Let P = pred S be the predecessor of S. Refer to Figure 1(a). C S and C P refer to their respective capacities, i.e., the number of requests they are able to process during one unit of time, respectively. The capacity of the peers is obviously fixed. At the end of each step, peers send the number of requests they received during this time unit, for each node it runs, to its successor: S has information on the loads of the nodes run by P, and obviously on its own load. Let us assume that, during last time unit τ, the set of nodes run by S and P were respectively νs τ and νp τ and that each n ντ S ντ P has received a different number of requests l n. Then, the load, denoted L τ S of S during the period τ was the sum of the loads of the nodes it runs, i.e., L τ S = n ν τ S l n. (1) We easily see that, during this step/time unit τ, the number of satisfied requests (or throughput T ), i.e., requests that were effectively processed until reaching its destination (or finding the service sought does not exist) is: T τ S,P = min(l τ S, C S ) + min(l τ P, C P ). (2) Starting from this knowledge, i.e., the load of every nodes n νs τ ντ P, we need to maximize the throughput of the next unit of time τ + 1. To do so, we need to find the new distribution (ν τ+1 S, ν τ+1 P ) that maximizes the throughput i.e., such that: = min( l n, C S ) + min( l m, C P ) (3) T τ+1 S,P n ν τ+1 S m ν τ+1 P is maximum, assuming the load distribution will be similar in times τ + 1 and τ, what of course can not be sure, but reasonably assumed. The number of possible distributions of nodes on peers is bounded by the fact that nodes identifiers can not be changed, in order to ensure the routing consistency and a way to retrieve services. The only parameter that we can change is the identifiers of peers. Then, as illustrated on Figure 1, finding the best distribution is equivalent to find the best position of P moving along the ring, as illustrated by arrows on Figure 1(b). The number of candidate positions for P is ν S ν P 1. Thus, the time and extra space complexity of the redistribution algorithm is clearly in O( νs τ ντ P ). In other ANR SPADES. 08-ANR-SEGI-025 Page 5

6 Figure 1: One local load balancing step. words, even if periodically performed, the MLT heuristic has, locally, a constant communication cost and a time complexity linear in the number of nodes between the two local peers. An example of the result of this process is given by Figure 1(c), where, according to the previous metric, the best distribution is three (weighted) nodes on S, and 5 (weighted) nodes on P. This heuristic is henceforth referred to as MLT (Max Local Throughput). 08-ANR-SEGI-025 Page 6

7 Simulations To validate our heuristic and better capture its efficiency, we developed a simulator of this architecture, into which we integrated two load balancing heuristics: MLT and an adaptation of a recent load balancing algorithm initially designed for DHTs known as the k-choices algorithm [2] and, to our knowledge, the most related existing heuristic. We denote KC this adaptation. More precisely, when used, KC is run each time a peer joins the system. Because some regions of the ring are more densely populated than others, KC finds, among k potential locations for the new peer, the one that leads to the best local load balance. Please refer to [2] for more details. We used a discrete time in the simulations. One simulation was run for a fixed number of time units. Each simulation were repeated at least 30 times, to have some relevant results. Recall that the capacity of a peer refers to the maximum number of requests processed by it during one time unit. All requests received on a peer after it has reached this number are ignored and considered as unsatisfied. The ratio between the most and the least powerful peer is 4. A request is said to be satisfied if it reaches its final destination. The number of peers is approximately 100, and the number of nodes around We set KC with k = 4 (which we believe is relevant, since, as established by Azar, Broder, Karlin and Upfal [1], beyond k = 2, the gain on the load balance is just a constant factor). The prefix trees are built with identifiers commonly encountered in a grid computing context such as names of linear algebra routines. Efficiency over a stable platform. We first estimated the global throughput of the system when using MLT, KC, and no explicit load balancing at all. Each time unit is composed of several steps. (1) In experiments where MLT is enabled, a fixed fraction of the peers executes the MLT load balancing. (2) A fixed fraction of peers join the system (applying the KC algorithm in experiments where it is enabled, or simply the basic protocol detailed in SPADES Report 1.3, otherwise.) (3) A fixed fraction of peers leaves the system. (4) A fixed fraction of new services are added in the tree (possibly resulting in the creation of new nodes). (5) Discovery requests are sent to the tree (and results on the number of satisfied discovery requests are collected). During first experiments, services requested were randomly picked among the set of available services. Figure 2 gives the percentage of satisfied requests using MLT, KC and no load balancing, for 50 time units. The first 10 units correspond to the period where the prefix tree is growing. After, it remains the same. The obtained curves show that using heuristics, and more particularly MLT, leads to a non negligible gain. Figure 3 shows the results of the same experiment, but with a very high number of requests, in order to stress the system. We observe similar results, even if the satisfaction percentage is obviously globally lower. Efficiency over a dynamic platform. Previous experiments were intentionally conducted in a stable network, without churn. While the efficiency of the KC algorithm relies on the dynamic nature of the system since load balancing is done each time a peer joins the system, we repeated the experiment under a high churn rate. Now, 10% of the nodes are replaced at each time unit. This is why we repeated these experiments with an increased fraction of peers joining and leaving the network. Figures 4 and 5 give the results of the same experiments than before but conducted over a dynamic platform. We see that KC performs a bit better ANR SPADES. 08-ANR-SEGI-025 Page 7

8 Percentage of satisfied requests Load balancing stable network no overload MLT enabled [30 runs] KC enabled [30 runs] No LB [30 runs] Percentage of satisfied requests Load balancing stable network overload MLT enabled [30 runs] KC enabled [30 runs] No LB [30 runs] Time Time Figure 2: Stable network, low load. Figure 3: Stable network, high load. than previously, and gives results similar to MLT. We conducted these experiments for different loads. The results are summarized in Table 1. The percentages in the left column express the ratio between the number of requests and the aggregated capacity of all peers in the system. The table gives the gain on the number of satisfied requests of each heuristic compared to the architecture with no load balancing. By reading the table, we observe that the gain can be really high, and that the MLT s gain is globally higher than the KC s gain. Percentage of satisfied requests Comparing LB algorithms dynamic network no overload MLT enabled [30 runs] KC enabled [30 runs] No LB [30 runs] Percentage of satisfied requests Comparing LB algorithms dynamic network overload MLT enabled [30 runs] KC enabled [30 runs] No LB [30 runs] Time Time Figure 4: Dynamic network, low load. Figure 5: Dynamic network, high load. Reactivity facing hot spots. Our last series of simulations, whose result is illustrated by Figure 6, consisted in creating hot spots in the tree, by temporarily launching many discovery requests on some keys stored in the same region of the tree, i.e., on lexicographically closed keys, in bursts. The experiment is divided in time as follows: during the first 40 time units, services are again randomly picked. Then, between 40 and 80, a hot spot is created on the particular S3L library. Most of S3L routines are named by a string beginning by S3L. We thus overloaded the subtree containing the keys prefixed by S3L. The network was 08-ANR-SEGI-025 Page 8

9 Load Stable network Dynamic network MLT KC MLT KC 5% 39,62% 38,58% 18.25% 32,47% 10% 103,41% 58,95% 46,16% 51,00% 16% 147,07% 64,97% 65,90% 59,11% 24% 165,25% 59,27% 71,26% 60,01% 40% 206,90% 68,16% 97,71% 67,18% 80% 230,51% 76,99% 90,59% 71,93% Table 1: Summary of gains of KC and MLT heuristics. previously balanced for random requests, and as a consequence, the number of satisfied requests suddenly falls. However, the MLT -enabled architecture adapts to the situation and increases the satisfaction ratio to a reasonable point, by moving more peers in the region of the S3L* nodes. A second change arises at time 80, when simulating the arrival of many requests on the ScaLAPACK library whose functions begin with P. The system reacts again and provide an improved throughput again. The random way to pick services is chosen for the 40 last time units, leading to a behavior similar to the one of the beginning. As less hotspots are created, the throughput quickly improves to reach the performance level of the beginning of the simulation. Avoiding Physical Communications by Lexicographic Clustering. Finally, as previously said, the mapping scheme is better in several ways than a random DHT-based mapping, since a random mapping results in breaking the locality of keys. Connected nodes in the tree are randomly dispatched in random locations of the physical network. With our mapping scheme, the set of nodes stored on one peer are highly connected. This fact brings about a reduction of the communications between peers, since a high amount of routing steps in the tree involves two nodes running on the same peer. Figure 7 gives, for each time unit, and following the same experimental scheme as in simulation with hot-spots, the average number of hops in the tree required to reach their final destination. The total number of hops is provided (between 8 and 10 for each request). We see that our self-contained mapping featured with MLT significantly reduces the amount of communications within the physical network (from approximately 9 to approximately 3), while the random DHT-based mapping reduces it only by approximately ANR-SEGI-025 Page 9

10 Percentage of Satisfied requests Load balancing dynamic network hot spots MLT enabled [50 runs] KC enabled [50 runs] No LB [50 runs] Hops Communication gain Logical hops [100 run] Physical hops random mapping [100 run] Physical hops lexico. mapping with LB (MLT) [100 run] Time Time Figure 6: spots. Dynamic network with hot Figure 7: Reduction of the communication by the lexicographic mapping. 08-ANR-SEGI-025 Page 10

11 Conclusion We have developed load-balancing techniques for the service discovery systems that takes heterogeneity and churn into account. Moreover, our technique has been shown to give better performance than the existing heuristics for a DHT context. ANR SPADES. 08-ANR-SEGI-025 Page 11

12 Bibliography [1] Y. Azar, A. Z. Broder, A. R. Karlin, and E. Upfal. Balanced Allocations. SIAM J. Comput., 29(1): , [2] J. Ledlie and M. I. Seltzer. Distributed, Secure Load Balancing with Skew, Heterogeneity and Churn. In INFOCOM, pages , ANR SPADES. 08-ANR-SEGI-025 Page 12

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 349 Load Balancing Heterogeneous Request in DHT-based P2P Systems Mrs. Yogita A. Dalvi Dr. R. Shankar Mr. Atesh

More information

A Peer-to-peer Extension of Network-Enabled Server Systems

A Peer-to-peer Extension of Network-Enabled Server Systems A Peer-to-peer Extension of Network-Enabled Server Systems Eddy Caron 1, Frédéric Desprez 1, Cédric Tedeschi 1 Franck Petit 2 1 - GRAAL Project / LIP laboratory 2 - LaRIA laboratory E-Science 2005 - December

More information

Load Balancing in Structured Overlay Networks. Tallat M. Shafaat tallat(@)kth.se

Load Balancing in Structured Overlay Networks. Tallat M. Shafaat tallat(@)kth.se Load Balancing in Structured Overlay Networks Tallat M. Shafaat tallat(@)kth.se Overview Background The problem : load imbalance Causes of load imbalance Solutions But first, some slides from previous

More information

International journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer.

International journal of Engineering Research-Online A Peer Reviewed International Journal Articles available online http://www.ijoer. RESEARCH ARTICLE ISSN: 2321-7758 GLOBAL LOAD DISTRIBUTION USING SKIP GRAPH, BATON AND CHORD J.K.JEEVITHA, B.KARTHIKA* Information Technology,PSNA College of Engineering & Technology, Dindigul, India Article

More information

8 Conclusion and Future Work

8 Conclusion and Future Work 8 Conclusion and Future Work This chapter concludes this thesis and provides an outlook on future work in the area of mobile ad hoc networks and peer-to-peer overlay networks 8.1 Conclusion Due to the

More information

CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT

CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT 81 CHAPTER 5 WLDMA: A NEW LOAD BALANCING STRATEGY FOR WAN ENVIRONMENT 5.1 INTRODUCTION Distributed Web servers on the Internet require high scalability and availability to provide efficient services to

More information

IMPROVED PROXIMITY AWARE LOAD BALANCING FOR HETEROGENEOUS NODES

IMPROVED PROXIMITY AWARE LOAD BALANCING FOR HETEROGENEOUS NODES www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 2 Issue 6 June, 2013 Page No. 1914-1919 IMPROVED PROXIMITY AWARE LOAD BALANCING FOR HETEROGENEOUS NODES Ms.

More information

2004 Networks UK Publishers. Reprinted with permission.

2004 Networks UK Publishers. Reprinted with permission. Riikka Susitaival and Samuli Aalto. Adaptive load balancing with OSPF. In Proceedings of the Second International Working Conference on Performance Modelling and Evaluation of Heterogeneous Networks (HET

More information

Fair Scheduling Algorithm with Dynamic Load Balancing Using In Grid Computing

Fair Scheduling Algorithm with Dynamic Load Balancing Using In Grid Computing Research Inventy: International Journal Of Engineering And Science Vol.2, Issue 10 (April 2013), Pp 53-57 Issn(e): 2278-4721, Issn(p):2319-6483, Www.Researchinventy.Com Fair Scheduling Algorithm with Dynamic

More information

High Throughput Computing on P2P Networks. Carlos Pérez Miguel carlos.perezm@ehu.es

High Throughput Computing on P2P Networks. Carlos Pérez Miguel carlos.perezm@ehu.es High Throughput Computing on P2P Networks Carlos Pérez Miguel carlos.perezm@ehu.es Overview High Throughput Computing Motivation All things distributed: Peer-to-peer Non structured overlays Structured

More information

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann

Storage Systems Autumn 2009. Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Storage Systems Autumn 2009 Chapter 6: Distributed Hash Tables and their Applications André Brinkmann Scaling RAID architectures Using traditional RAID architecture does not scale Adding news disk implies

More information

Comparison on Different Load Balancing Algorithms of Peer to Peer Networks

Comparison on Different Load Balancing Algorithms of Peer to Peer Networks Comparison on Different Load Balancing Algorithms of Peer to Peer Networks K.N.Sirisha *, S.Bhagya Rekha M.Tech,Software Engineering Noble college of Engineering & Technology for Women Web Technologies

More information

Stability of QOS. Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu

Stability of QOS. Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu Stability of QOS Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu Abstract Given a choice between two services, rest of the things being equal, it is natural to prefer the one with more

More information

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster

A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster , pp.11-20 http://dx.doi.org/10.14257/ ijgdc.2014.7.2.02 A Load Balancing Algorithm based on the Variation Trend of Entropy in Homogeneous Cluster Kehe Wu 1, Long Chen 2, Shichao Ye 2 and Yi Li 2 1 Beijing

More information

LOAD BALANCING WITH PARTIAL KNOWLEDGE OF SYSTEM

LOAD BALANCING WITH PARTIAL KNOWLEDGE OF SYSTEM LOAD BALANCING WITH PARTIAL KNOWLEDGE OF SYSTEM IN PEER TO PEER NETWORKS R. Vijayalakshmi and S. Muthu Kumarasamy Dept. of Computer Science & Engineering, S.A. Engineering College Anna University, Chennai,

More information

PSON: A Scalable Peer-to-Peer File Sharing System Supporting Complex Queries

PSON: A Scalable Peer-to-Peer File Sharing System Supporting Complex Queries PSON: A Scalable Peer-to-Peer File Sharing System Supporting Complex Queries Jyoti Ahuja, Jun-Hong Cui, Shigang Chen, Li Lao jyoti@engr.uconn.edu, jcui@cse.uconn.edu, sgchen@cise.ufl.edu, llao@cs.ucla.edu

More information

An Optimization Model of Load Balancing in P2P SIP Architecture

An Optimization Model of Load Balancing in P2P SIP Architecture An Optimization Model of Load Balancing in P2P SIP Architecture 1 Kai Shuang, 2 Liying Chen *1, First Author, Corresponding Author Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn

More information

Load Balancing in Structured P2P Systems

Load Balancing in Structured P2P Systems 1 Load Balancing in Structured P2P Systems Ananth Rao Karthik Lakshminarayanan Sonesh Surana Richard Karp Ion Stoica ananthar, karthik, sonesh, karp, istoica @cs.berkeley.edu Abstract Most P2P systems

More information

Path Selection Methods for Localized Quality of Service Routing

Path Selection Methods for Localized Quality of Service Routing Path Selection Methods for Localized Quality of Service Routing Xin Yuan and Arif Saifee Department of Computer Science, Florida State University, Tallahassee, FL Abstract Localized Quality of Service

More information

Distributed Computing over Communication Networks: Topology. (with an excursion to P2P)

Distributed Computing over Communication Networks: Topology. (with an excursion to P2P) Distributed Computing over Communication Networks: Topology (with an excursion to P2P) Some administrative comments... There will be a Skript for this part of the lecture. (Same as slides, except for today...

More information

PEER-TO-PEER (P2P) systems have emerged as an appealing

PEER-TO-PEER (P2P) systems have emerged as an appealing IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 4, APRIL 2009 595 Histogram-Based Global Load Balancing in Structured Peer-to-Peer Systems Quang Hieu Vu, Member, IEEE, Beng Chin Ooi,

More information

Motivation for peer-to-peer

Motivation for peer-to-peer Peer-to-peer systems INF 5040 autumn 2007 lecturer: Roman Vitenberg INF5040, Frank Eliassen & Roman Vitenberg 1 Motivation for peer-to-peer Inherent restrictions of the standard client/server model Centralised

More information

Load Balancing in Structured Peer to Peer Systems

Load Balancing in Structured Peer to Peer Systems Load Balancing in Structured Peer to Peer Systems DR.K.P.KALIYAMURTHIE 1, D.PARAMESWARI 2 Professor and Head, Dept. of IT, Bharath University, Chennai-600 073 1 Asst. Prof. (SG), Dept. of Computer Applications,

More information

1. Comments on reviews a. Need to avoid just summarizing web page asks you for:

1. Comments on reviews a. Need to avoid just summarizing web page asks you for: 1. Comments on reviews a. Need to avoid just summarizing web page asks you for: i. A one or two sentence summary of the paper ii. A description of the problem they were trying to solve iii. A summary of

More information

Giving life to today s media distribution services

Giving life to today s media distribution services Giving life to today s media distribution services FIA - Future Internet Assembly Athens, 17 March 2014 Presenter: Nikolaos Efthymiopoulos Network architecture & Management Group Copyright University of

More information

A Review on Load Balancing In Cloud Computing 1

A Review on Load Balancing In Cloud Computing 1 www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 6 June 2015, Page No. 12333-12339 A Review on Load Balancing In Cloud Computing 1 Peenaz Pathak, 2 Er.Kamna

More information

Multi-service Load Balancing in a Heterogeneous Network with Vertical Handover

Multi-service Load Balancing in a Heterogeneous Network with Vertical Handover 1 Multi-service Load Balancing in a Heterogeneous Network with Vertical Handover Jie Xu, Member, IEEE, Yuming Jiang, Member, IEEE, and Andrew Perkis, Member, IEEE Abstract In this paper we investigate

More information

Using Peer to Peer Dynamic Querying in Grid Information Services

Using Peer to Peer Dynamic Querying in Grid Information Services Using Peer to Peer Dynamic Querying in Grid Information Services Domenico Talia and Paolo Trunfio DEIS University of Calabria HPC 2008 July 2, 2008 Cetraro, Italy Using P2P for Large scale Grid Information

More information

Locality-Aware Randomized Load Balancing Algorithms for DHT Networks

Locality-Aware Randomized Load Balancing Algorithms for DHT Networks Locality-Aware ized Load Balancing Algorithms for DHT Networks Haiying Shen and Cheng-Zhong Xu Department of Electrical & Computer Engineering Wayne State University, Detroit, MI 4822 {shy,czxu}@ece.eng.wayne.edu

More information

DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS

DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS Markus Esch, Eric Tobias - University of Luxembourg MOTIVATION HyperVerse project Massive Multiuser

More information

RELOAD Usages for P2P Data Storage and Discovery

RELOAD Usages for P2P Data Storage and Discovery RELOAD Usages for P2P Data Storage and Discovery AW2-presentation from Alexander Knauf Alexander.Knauf@Haw-Hamburg.de Review Infrastructure Independent Conferencing: Decentralized fashion Multiple conference

More information

CHAPTER 2. QoS ROUTING AND ITS ROLE IN QOS PARADIGM

CHAPTER 2. QoS ROUTING AND ITS ROLE IN QOS PARADIGM CHAPTER 2 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 22 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 2.1 INTRODUCTION As the main emphasis of the present research work is on achieving QoS in routing, hence this

More information

A Content-Based Load Balancing Algorithm for Metadata Servers in Cluster File Systems*

A Content-Based Load Balancing Algorithm for Metadata Servers in Cluster File Systems* A Content-Based Load Balancing Algorithm for Metadata Servers in Cluster File Systems* Junho Jang, Saeyoung Han, Sungyong Park, and Jihoon Yang Department of Computer Science and Interdisciplinary Program

More information

Varalakshmi.T #1, Arul Murugan.R #2 # Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam

Varalakshmi.T #1, Arul Murugan.R #2 # Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam A Survey on P2P File Sharing Systems Using Proximity-aware interest Clustering Varalakshmi.T #1, Arul Murugan.R #2 # Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam

More information

Lecture 2 February 12, 2003

Lecture 2 February 12, 2003 6.897: Advanced Data Structures Spring 003 Prof. Erik Demaine Lecture February, 003 Scribe: Jeff Lindy Overview In the last lecture we considered the successor problem for a bounded universe of size u.

More information

A Survey Of Various Load Balancing Algorithms In Cloud Computing

A Survey Of Various Load Balancing Algorithms In Cloud Computing A Survey Of Various Load Balancing Algorithms In Cloud Computing Dharmesh Kashyap, Jaydeep Viradiya Abstract: Cloud computing is emerging as a new paradigm for manipulating, configuring, and accessing

More information

Adaptive Load Balancing in KAD

Adaptive Load Balancing in KAD Adaptive Load Balancing in KAD Damiano Carra University of Verona Verona, Italy damiano.carra@univr.it Moritz Steiner Bell Labs Alcatel-Lucent, USA moritz@bell-labs.com Pietro Michiardi Eurecom Sophia

More information

The p2pweb model: a glue for the Web

The p2pweb model: a glue for the Web The p2pweb model: a glue for the Web Pedro García López, Marc Sánchez Artigas, Jordi Pujol Ahulló Universitat Rovira i Virgili Tarragona, Spain { pedro.garcia, marc.sanchez, jordi.pujol}@urv.cat Abstract

More information

Load Balancing. Load Balancing 1 / 24

Load Balancing. Load Balancing 1 / 24 Load Balancing Backtracking, branch & bound and alpha-beta pruning: how to assign work to idle processes without much communication? Additionally for alpha-beta pruning: implementing the young-brothers-wait

More information

Load Balancing in Structured Peer to Peer Systems

Load Balancing in Structured Peer to Peer Systems Load Balancing in Structured Peer to Peer Systems Dr.K.P.Kaliyamurthie 1, D.Parameswari 2 1.Professor and Head, Dept. of IT, Bharath University, Chennai-600 073. 2.Asst. Prof.(SG), Dept. of Computer Applications,

More information

There are a number of factors that increase the risk of performance problems in complex computer and software systems, such as e-commerce systems.

There are a number of factors that increase the risk of performance problems in complex computer and software systems, such as e-commerce systems. ASSURING PERFORMANCE IN E-COMMERCE SYSTEMS Dr. John Murphy Abstract Performance Assurance is a methodology that, when applied during the design and development cycle, will greatly increase the chances

More information

A Parameter-Free Load Balancing Mechanism For P2P Networks

A Parameter-Free Load Balancing Mechanism For P2P Networks A Parameter-Free Load Balancing Mechanism For P2P Networks Tyler Steele, Vivek Vishnumurthy and Paul Francis Department of Computer Science, Cornell University, Ithaca, NY 14853 {ths22,francis,vivi}@cs.cornell.edu

More information

Load Balancing in Peer-to-Peer Data Networks

Load Balancing in Peer-to-Peer Data Networks Load Balancing in Peer-to-Peer Data Networks David Novák Masaryk University, Brno, Czech Republic xnovak8@fi.muni.cz Abstract. One of the issues considered in all Peer-to-Peer Data Networks, or Structured

More information

EQ-BGP: an efficient inter-domain QoS routing protocol

EQ-BGP: an efficient inter-domain QoS routing protocol EQ-BGP: an efficient inter-domain QoS routing protocol Andrzej Beben Institute of Telecommunications Warsaw University of Technology Nowowiejska 15/19, 00-665 Warsaw, Poland abeben@tele.pw.edu.pl Abstract

More information

Grid Computing Approach for Dynamic Load Balancing

Grid Computing Approach for Dynamic Load Balancing International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-1 E-ISSN: 2347-2693 Grid Computing Approach for Dynamic Load Balancing Kapil B. Morey 1*, Sachin B. Jadhav

More information

Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems

Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems Kunwadee Sripanidkulchai Bruce Maggs Hui Zhang Carnegie Mellon University, Pittsburgh, PA 15213 {kunwadee,bmm,hzhang}@cs.cmu.edu

More information

A Load Balancing Method in SiCo Hierarchical DHT-based P2P Network

A Load Balancing Method in SiCo Hierarchical DHT-based P2P Network 1 Shuang Kai, 2 Qu Zheng *1, Shuang Kai Beijing University of Posts and Telecommunications, shuangk@bupt.edu.cn 2, Qu Zheng Beijing University of Posts and Telecommunications, buptquzheng@gmail.com Abstract

More information

Performance of networks containing both MaxNet and SumNet links

Performance of networks containing both MaxNet and SumNet links Performance of networks containing both MaxNet and SumNet links Lachlan L. H. Andrew and Bartek P. Wydrowski Abstract Both MaxNet and SumNet are distributed congestion control architectures suitable for

More information

Evaluation of Unlimited Storage: Towards Better Data Access Model for Sensor Network

Evaluation of Unlimited Storage: Towards Better Data Access Model for Sensor Network Evaluation of Unlimited Storage: Towards Better Data Access Model for Sensor Network Sagar M Mane Walchand Institute of Technology Solapur. India. Solapur University, Solapur. S.S.Apte Walchand Institute

More information

A Topology-Aware Relay Lookup Scheme for P2P VoIP System

A Topology-Aware Relay Lookup Scheme for P2P VoIP System Int. J. Communications, Network and System Sciences, 2010, 3, 119-125 doi:10.4236/ijcns.2010.32018 Published Online February 2010 (http://www.scirp.org/journal/ijcns/). A Topology-Aware Relay Lookup Scheme

More information

International Journal of Advance Research in Computer Science and Management Studies

International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 6, June 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

Lecture 3: Scaling by Load Balancing 1. Comments on reviews i. 2. Topic 1: Scalability a. QUESTION: What are problems? i. These papers look at

Lecture 3: Scaling by Load Balancing 1. Comments on reviews i. 2. Topic 1: Scalability a. QUESTION: What are problems? i. These papers look at Lecture 3: Scaling by Load Balancing 1. Comments on reviews i. 2. Topic 1: Scalability a. QUESTION: What are problems? i. These papers look at distributing load b. QUESTION: What is the context? i. How

More information

Optimizing and Balancing Load in Fully Distributed P2P File Sharing Systems

Optimizing and Balancing Load in Fully Distributed P2P File Sharing Systems Optimizing and Balancing Load in Fully Distributed P2P File Sharing Systems (Scalable and Efficient Keyword Searching) Anh-Tuan Gai INRIA Rocquencourt anh-tuan.gai@inria.fr Laurent Viennot INRIA Rocquencourt

More information

Peer-to-Peer Networks. Chapter 6: P2P Content Distribution

Peer-to-Peer Networks. Chapter 6: P2P Content Distribution Peer-to-Peer Networks Chapter 6: P2P Content Distribution Chapter Outline Content distribution overview Why P2P content distribution? Network coding Peer-to-peer multicast Kangasharju: Peer-to-Peer Networks

More information

New Structured P2P Network with Dynamic Load Balancing Scheme

New Structured P2P Network with Dynamic Load Balancing Scheme New Structured P2P Network with Dynamic Load Balancing Scheme Atushi TAKEDA, Takuma OIDE and Akiko TAKAHASHI Department of Information Science, Tohoku Gakuin University Department of Information Engineering,

More information

KDDCS: A Load-Balanced In-Network Data-Centric Storage Scheme for Sensor Networks

KDDCS: A Load-Balanced In-Network Data-Centric Storage Scheme for Sensor Networks KDDCS: A Load-Balanced In-Network Data-Centric Storage Scheme for Sensor Networks Mohamed Aly Kirk Pruhs Panos K. Chrysanthis Department of Computer Science University of Pittsburgh Pittsburgh, PA 1526,

More information

Object Request Reduction in Home Nodes and Load Balancing of Object Request in Hybrid Decentralized Web Caching

Object Request Reduction in Home Nodes and Load Balancing of Object Request in Hybrid Decentralized Web Caching 2012 2 nd International Conference on Information Communication and Management (ICICM 2012) IPCSIT vol. 55 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V55.5 Object Request Reduction

More information

CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS

CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS 137 CHAPTER 8 CONCLUSION AND FUTURE ENHANCEMENTS 8.1 CONCLUSION In this thesis, efficient schemes have been designed and analyzed to control congestion and distribute the load in the routing process of

More information

Achieving Resilient and Efficient Load Balancing in DHT-based P2P Systems

Achieving Resilient and Efficient Load Balancing in DHT-based P2P Systems Achieving Resilient and Efficient Load Balancing in DHT-based P2P Systems Di Wu, Ye Tian and Kam-Wing Ng Department of Computer Science & Engineering The Chinese University of Hong Kong Shatin, N.T., Hong

More information

Quality of Service versus Fairness. Inelastic Applications. QoS Analogy: Surface Mail. How to Provide QoS?

Quality of Service versus Fairness. Inelastic Applications. QoS Analogy: Surface Mail. How to Provide QoS? 18-345: Introduction to Telecommunication Networks Lectures 20: Quality of Service Peter Steenkiste Spring 2015 www.cs.cmu.edu/~prs/nets-ece Overview What is QoS? Queuing discipline and scheduling Traffic

More information

CHAPTER 6 SECURE PACKET TRANSMISSION IN WIRELESS SENSOR NETWORKS USING DYNAMIC ROUTING TECHNIQUES

CHAPTER 6 SECURE PACKET TRANSMISSION IN WIRELESS SENSOR NETWORKS USING DYNAMIC ROUTING TECHNIQUES CHAPTER 6 SECURE PACKET TRANSMISSION IN WIRELESS SENSOR NETWORKS USING DYNAMIC ROUTING TECHNIQUES 6.1 Introduction The process of dispersive routing provides the required distribution of packets rather

More information

Parallel Scalable Algorithms- Performance Parameters

Parallel Scalable Algorithms- Performance Parameters www.bsc.es Parallel Scalable Algorithms- Performance Parameters Vassil Alexandrov, ICREA - Barcelona Supercomputing Center, Spain Overview Sources of Overhead in Parallel Programs Performance Metrics for

More information

VIRTUALIZATION [1] has been touted as a revolutionary

VIRTUALIZATION [1] has been touted as a revolutionary Supporting Seamless Virtual Machine Migration via Named Data Networking in Cloud Data Center Ruitao Xie, Yonggang Wen, Xiaohua Jia, Fellow, IEEE and Haiyong Xie 1 Abstract Virtual machine migration has

More information

IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION

IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND SCIENCE IMPACT OF DISTRIBUTED SYSTEMS IN MANAGING CLOUD APPLICATION N.Vijaya Sunder Sagar 1, M.Dileep Kumar 2, M.Nagesh 3, Lunavath Gandhi

More information

On the effect of forwarding table size on SDN network utilization

On the effect of forwarding table size on SDN network utilization IBM Haifa Research Lab On the effect of forwarding table size on SDN network utilization Rami Cohen IBM Haifa Research Lab Liane Lewin Eytan Yahoo Research, Haifa Seffi Naor CS Technion, Israel Danny Raz

More information

Load Balancing in Distributed Web Server Systems With Partial Document Replication

Load Balancing in Distributed Web Server Systems With Partial Document Replication Load Balancing in Distributed Web Server Systems With Partial Document Replication Ling Zhuo, Cho-Li Wang and Francis C. M. Lau Department of Computer Science and Information Systems The University of

More information

Operating Systems. Virtual Memory

Operating Systems. Virtual Memory Operating Systems Virtual Memory Virtual Memory Topics. Memory Hierarchy. Why Virtual Memory. Virtual Memory Issues. Virtual Memory Solutions. Locality of Reference. Virtual Memory with Segmentation. Page

More information

www.modelingconcepts.com A Quick Chat about SOMF Capabilities Page1 Service-Oriented Modeling Framework (SOMF) Building Attribution Models

www.modelingconcepts.com A Quick Chat about SOMF Capabilities Page1 Service-Oriented Modeling Framework (SOMF) Building Attribution Models Page1 www.modelingconcepts.com A Quick Chat about SOMF Capabilities Constructing a Service-Oriented Attribution Model Training material for architects, business analysts, system analysts, software developers,

More information

Using Content-Addressable Networks for Load Balancing in Desktop Grids (Extended Version)

Using Content-Addressable Networks for Load Balancing in Desktop Grids (Extended Version) Using Content-Addressable Networks for Load Balancing in Desktop Grids (Extended Version) Jik-Soo Kim, Peter Keleher, Michael Marsh, Bobby Bhattacharjee and Alan Sussman UMIACS and Department of Computer

More information

SCALABLE RANGE QUERY PROCESSING FOR LARGE-SCALE DISTRIBUTED DATABASE APPLICATIONS *

SCALABLE RANGE QUERY PROCESSING FOR LARGE-SCALE DISTRIBUTED DATABASE APPLICATIONS * SCALABLE RANGE QUERY PROCESSING FOR LARGE-SCALE DISTRIBUTED DATABASE APPLICATIONS * Maha Abdallah LIP6, Université Paris 6, rue du Capitaine Scott 75015 Paris, France Maha.Abdallah@lip6.fr Hung Cuong Le

More information

LOAD BALANCING AS A STRATEGY LEARNING TASK

LOAD BALANCING AS A STRATEGY LEARNING TASK LOAD BALANCING AS A STRATEGY LEARNING TASK 1 K.KUNGUMARAJ, 2 T.RAVICHANDRAN 1 Research Scholar, Karpagam University, Coimbatore 21. 2 Principal, Hindusthan Institute of Technology, Coimbatore 32. ABSTRACT

More information

Service Layer Components for Resource Management in Distributed Applications

Service Layer Components for Resource Management in Distributed Applications Corporate Technology Service Layer Components for Resource Management in Distributed Applications Fabian Stäber Siemens Corporate Technology, Information and Communications Copyright Siemens AG 2007. Alle

More information

System Interconnect Architectures. Goals and Analysis. Network Properties and Routing. Terminology - 2. Terminology - 1

System Interconnect Architectures. Goals and Analysis. Network Properties and Routing. Terminology - 2. Terminology - 1 System Interconnect Architectures CSCI 8150 Advanced Computer Architecture Hwang, Chapter 2 Program and Network Properties 2.4 System Interconnect Architectures Direct networks for static connections Indirect

More information

Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.

Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age. Volume 3, Issue 10, October 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Load Measurement

More information

Algorithms for Assigning Substrate Network Resources to Virtual Network Components

Algorithms for Assigning Substrate Network Resources to Virtual Network Components Algorithms for Assigning Substrate Network Resources to Virtual Network Components Yong Zhu and Mostafa Ammar Networking and Telecommunications Group Georgia Institute of Technology, Atlanta, Georgia Email:

More information

Load Distribution in Large Scale Network Monitoring Infrastructures

Load Distribution in Large Scale Network Monitoring Infrastructures Load Distribution in Large Scale Network Monitoring Infrastructures Josep Sanjuàs-Cuxart, Pere Barlet-Ros, Gianluca Iannaccone, and Josep Solé-Pareta Universitat Politècnica de Catalunya (UPC) {jsanjuas,pbarlet,pareta}@ac.upc.edu

More information

Distributed Hash Tables in P2P Systems - A literary survey

Distributed Hash Tables in P2P Systems - A literary survey Distributed Hash Tables in P2P Systems - A literary survey Timo Tanner Helsinki University of Technology tstanner@cc.hut.fi Abstract Distributed Hash Tables (DHT) are algorithms used in modern peer-to-peer

More information

RESERV: A Distributed, Load Balanced Information System for Grid Applications

RESERV: A Distributed, Load Balanced Information System for Grid Applications RESERV: A Distributed, Load Balanced Information System for Grid Applications Gábor Vincze, Zoltán Novák, Zoltán Pap, Rolland Vida Department of Telecommunications and Media Informatics, Budapest University

More information

Improving Query Processing Performance in Large Distributed Database Management Systems

Improving Query Processing Performance in Large Distributed Database Management Systems Norvald H. Ryeng Improving Query Processing Performance in Large Distributed Database Management Systems Thesis for the degree of Philosophiae Doctor Trondheim, November 2011 Norwegian University of Science

More information

A NOVEL RESOURCE EFFICIENT DMMS APPROACH

A NOVEL RESOURCE EFFICIENT DMMS APPROACH A NOVEL RESOURCE EFFICIENT DMMS APPROACH FOR NETWORK MONITORING AND CONTROLLING FUNCTIONS Golam R. Khan 1, Sharmistha Khan 2, Dhadesugoor R. Vaman 3, and Suxia Cui 4 Department of Electrical and Computer

More information

A hierarchical multicriteria routing model with traffic splitting for MPLS networks

A hierarchical multicriteria routing model with traffic splitting for MPLS networks A hierarchical multicriteria routing model with traffic splitting for MPLS networks João Clímaco, José Craveirinha, Marta Pascoal jclimaco@inesccpt, jcrav@deecucpt, marta@matucpt University of Coimbra

More information

Analysis of Algorithms I: Binary Search Trees

Analysis of Algorithms I: Binary Search Trees Analysis of Algorithms I: Binary Search Trees Xi Chen Columbia University Hash table: A data structure that maintains a subset of keys from a universe set U = {0, 1,..., p 1} and supports all three dictionary

More information

Christian Bettstetter. Mobility Modeling, Connectivity, and Adaptive Clustering in Ad Hoc Networks

Christian Bettstetter. Mobility Modeling, Connectivity, and Adaptive Clustering in Ad Hoc Networks Christian Bettstetter Mobility Modeling, Connectivity, and Adaptive Clustering in Ad Hoc Networks Contents 1 Introduction 1 2 Ad Hoc Networking: Principles, Applications, and Research Issues 5 2.1 Fundamental

More information

Effective Load Balancing in P2P Systems

Effective Load Balancing in P2P Systems Effective Load Balancing in P2P Systems Zhiyong Xu Suffolk University zxu@mcs.suffolk.edu Laxmi Bhuyan University of California, Riverside bhuyan@cs.ucr.edu Abstract In DHT based P2P systems, various issues

More information

Load balancing as a strategy learning task

Load balancing as a strategy learning task Scholarly Journal of Scientific Research and Essay (SJSRE) Vol. 1(2), pp. 30-34, April 2012 Available online at http:// www.scholarly-journals.com/sjsre ISSN 2315-6163 2012 Scholarly-Journals Review Load

More information

Chapter 5. Simple Ad hoc Key Management. 5.1 Introduction

Chapter 5. Simple Ad hoc Key Management. 5.1 Introduction Chapter 5 Simple Ad hoc Key Management 5.1 Introduction One of the most important consequences of the nature of the MANET networks is that one cannot assume that a node that is part of a network will be

More information

A Scalable Monitoring Approach Based on Aggregation and Refinement

A Scalable Monitoring Approach Based on Aggregation and Refinement IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL 20, NO 4, MAY 2002 677 A Scalable Monitoring Approach Based on Aggregation and Refinement Yow-Jian Lin, Member, IEEE and Mun Choon Chan, Member, IEEE

More information

Load Balancing Between Heterogenous Computing Clusters

Load Balancing Between Heterogenous Computing Clusters Load Balancing Between Heterogenous Computing Clusters Siu-Cheung Chau Dept. of Physics and Computing, Wilfrid Laurier University, Waterloo, Ontario, Canada, N2L 3C5 e-mail: schau@wlu.ca Ada Wai-Chee Fu

More information

query enabled P2P networks 2009. 08. 27 Park, Byunggyu

query enabled P2P networks 2009. 08. 27 Park, Byunggyu Load balancing mechanism in range query enabled P2P networks 2009. 08. 27 Park, Byunggyu Background Contents DHT(Distributed Hash Table) Motivation Proposed scheme Compression based Hashing Load balancing

More information

Improved Hybrid Dynamic Load Balancing Algorithm for Distributed Environment

Improved Hybrid Dynamic Load Balancing Algorithm for Distributed Environment International Journal of Scientific and Research Publications, Volume 3, Issue 3, March 2013 1 Improved Hybrid Dynamic Load Balancing Algorithm for Distributed Environment UrjashreePatil*, RajashreeShedge**

More information

MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS

MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS 1 M.LAKSHMI, 2 N.LAKSHMI 1 Assitant Professor, Dept.of.Computer science, MCC college.pattukottai. 2 Research Scholar, Dept.of.Computer science, MCC college.pattukottai.

More information

Network Coding for Large Scale Content Distribution

Network Coding for Large Scale Content Distribution Network Coding for Large Scale Content Distribution Christos Gkantsidis College of Computing Georgia Institute of Technology Atlanta, GA, 30332, USA Email: gantsich@cc.gatech.edu IEEE Infocom 2005 Pablo

More information

Routing in Switched Networks

Routing in Switched Networks Routing in Switched Networks Chapter 12 CS420/520 Axel Krings Page 1 Routing in Circuit Switched Network Many connections will need paths through more than one switch Need to find a route Efficiency Resilience

More information

A Workload-Based Adaptive Load-Balancing Technique for Mobile Ad Hoc Networks

A Workload-Based Adaptive Load-Balancing Technique for Mobile Ad Hoc Networks A Workload-Based Adaptive Load-Balancing Technique for Mobile Ad Hoc Networks Young J. Lee and George F. Riley School of Electrical & Computer Engineering Georgia Institute of Technology, Atlanta, GA 30332

More information

A SIMULATOR FOR LOAD BALANCING ANALYSIS IN DISTRIBUTED SYSTEMS

A SIMULATOR FOR LOAD BALANCING ANALYSIS IN DISTRIBUTED SYSTEMS Mihai Horia Zaharia, Florin Leon, Dan Galea (3) A Simulator for Load Balancing Analysis in Distributed Systems in A. Valachi, D. Galea, A. M. Florea, M. Craus (eds.) - Tehnologii informationale, Editura

More information

A Review on Load Balancing Algorithms in Cloud

A Review on Load Balancing Algorithms in Cloud A Review on Load Balancing Algorithms in Cloud Hareesh M J Dept. of CSE, RSET, Kochi hareeshmjoseph@ gmail.com John P Martin Dept. of CSE, RSET, Kochi johnpm12@gmail.com Yedhu Sastri Dept. of IT, RSET,

More information

Impact of Mobility Management on Heterogeneous Wireless Network

Impact of Mobility Management on Heterogeneous Wireless Network Impact of Mobility Management on Heterogeneous Wireless Network Mrs.Viddhulata Mohite 1,Research Scholar, Bharati Vidyapeeth University College of Enginnering, Pune,India Email:viddhulata.mohite@gmail.com

More information

Scalable Source Routing

Scalable Source Routing Scalable Source Routing January 2010 Thomas Fuhrmann Department of Informatics, Self-Organizing Systems Group, Technical University Munich, Germany Routing in Networks You re there. I m here. Scalable

More information

JMulTi/JStatCom - A Data Analysis Toolkit for End-users and Developers

JMulTi/JStatCom - A Data Analysis Toolkit for End-users and Developers JMulTi/JStatCom - A Data Analysis Toolkit for End-users and Developers Technology White Paper JStatCom Engineering, www.jstatcom.com by Markus Krätzig, June 4, 2007 Abstract JStatCom is a software framework

More information

Performance Evaluation of Mobile Agent-based Dynamic Load Balancing Algorithm

Performance Evaluation of Mobile Agent-based Dynamic Load Balancing Algorithm Performance Evaluation of Mobile -based Dynamic Load Balancing Algorithm MAGDY SAEB, CHERINE FATHY Computer Engineering Department Arab Academy for Science, Technology & Maritime Transport Alexandria,

More information